Tech News

Amazon has chosen recipients from all over the world to be awarded the Alexa fellowships. The Alexa Fellowships program is open for PhD and post-doctoral students specializing in conversational AI at select universities. The program was launched last year, when four researchers won awards. Amazon's Alexa Graduate fellowship The Alexa Graduate Fellowship supports conversational AI research by providing funds and mentorship to PhD and postdoctoral students. Faculty Advisors and Alexa Graduate Fellows will also teach conversational AI to undergraduate and graduate students using the Alexa Skills Kit (ASK) and Alexa Voice Services (AVS). The graduate fellowship recipients are selected based on their research interests, planned coursework and existing conversational AI curriculum. This year the institutions include six in the United States, two in the United Kingdom, one in Canada and one in India. The 10 universities are: Carnegie Mellon University, Pittsburgh, PA International Institute of Information Technology, Hyderabad, India Johns Hopkins University, Baltimore, MD MIT App Inventor, Boston, MA University of Cambridge, Cambridge, United Kingdom University of Sheffield, Sheffield, United Kingdom University of Southern California, Los Angeles, CA University of Texas at Austin, Austin, TX University of Washington, Seattle, WA University of Waterloo, Waterloo, Ontario, Canada Amazon's Alexa Innovation Fellowship The Alexa Innovation Fellowship is dedicated to innovations in conversational AI. The program was introduced this year and Amazon has partnered with university entrepreneurship centers to help student-led startups build their innovative conversational interfaces. The fellowship also provides resources to faculty members. This year ten leading entrepreneurship center faculty members were selected as the inaugural class of Alexa Innovation Fellows. They are invited to learn from the Alexa team and network with successful Alexa Fund entrepreneurs. Instructors will receive funding, Alexa devices, hardware kits and regular training, as well as introductions to successful Alexa Fund-backed entrepreneurs. The 10 universities selected to receive the 2018-2019 Alexa Innovation Fellowship include: Arizona State University, Tempe, AZ California State University, Northridge, CA Carnegie Mellon University, Pittsburgh, PA Dartmouth College, Hanover, NH Emerson College, Boston, MA Texas A&M University, College Station, TX University of California, Berkeley, CA University of Illinois, Urbana-Champaign, IL University of Michigan, Ann Arbor, MI University of Southern California, Los Angeles, CA “We want to make it easier and more accessible for smart people outside of the company to get involved with conversational AI. That's why we launched the Alexa Skills Kit (ASK) and Alexa Voice Services (AVS) and allocated $200 million to promising startups innovating with voice via the Alexa Fund.” wrote Kevin Crews, Senior Product Manager for the Amazon Alexa Fellowship, in a blog post. Read more about the 2018-2019 Alexa Fellowship class on the Amazon blog. Read next Cortana and Alexa become best friends: Microsoft and Amazon release a preview of this integration Voice, natural language, and conversations: Are they the next web UI?

After FireEye’s announcement on a suspected influence operation (using a network of fake news sites) in Iran two weeks ago, Reddit started its own investigation into these suspicious websites. Just two days ago, Reddit has shared the findings of its investigation. It has also consulted with third parties to dig deeper into the matter and get more relevant information. The influence group in Iran is leveraging the inauthentic websites “to promote political narratives in line with Iranian Interests”. These narratives comprises of anti-Saudi, anti-Israeli, and pro-Palestinian themes. It also provides support for U.S. policies which are favorable to Iran, such as the U.S.-Iran nuclear deal (JCPOA). According to Reddit, 143 accounts have been uncovered so far that are suspected to be linked to this influence group. The majority (126) of these accounts were created between 2015 and 2018, with a few (17) of these accounts dating back to 2011. More than 51 accounts were banned by Reddit before beginning the investigation as part of their trust and safety practices. Additionally no ads were posted by these accounts on Reddit. These groups were found to be focussed on discussing subjects that are important to Iran such as criticism of US policies in the Middle East, negative sentiment toward Saudi Arabia and Israel and discussions regarding Syria and ISIS. Around 60% of the accounts had karma below 1,000 out of which 36% of these accounts had zero or negative karma. However, a minority of 40% of the accounts had more than 1,000 karma. Reddit is planning to keep these accounts with varied karma levels public. This is to make the moderators, investigators, and the users on Reddit more aware of the tactics that foreign agents could attempt to use. However, Reddit will be removing some accounts in the future. Reddit found the behavior of these accounts quite different in the sense that even though the overall influence of these accounts was low, some of these accounts were still able to gain traction. It was noted that these accounts would share news and articles aligned to Iran’s political narrative such as highlighting civilian deaths in Yemen. The investigation is, according to Reddit, a tribute to the “incredible vigilance” of the Reddit community. Reddit is now planning to develop a trusted reporter system which will be able to better separate useful information from the junk. They’re also investing in advanced detection and mitigation methods. “Our actions against these threats may not always be immediately visible to you, but this is a battle we have been fighting, and will continue to fight for the foreseeable future. And of course, we’ll continue to communicate openly with you about these subjects” says the Reddit team. For more information, read the official FireEye report. Read next Google’s Protect your Election program: Security policies to defend against state-sponsored phishing attacks, and influence campaigns Intel faces backlash on Microcode Patches after it prohibited Benchmarking or comparison

The Senate Select Committee on Intelligence will be conducting a hearing on alleged Russian interference in the 2016 U.S. presidential election this week on September 5. Top executives in the tech industry including Twitter CEO Jack Dorsey and Facebook COO Sheryl Sandberg will be testifying this hearing. Google CEO Sundar Pichai has, so far, declined to accept the Senate committee's invitation. Like last year, Google’s top lawyer, Kent Walker, the senior vice president of global affairs, will be testifying in place of Pichai. Google has not officially released a statement as to why Pichai or his boss, Alphabet CEO Larry Page, are not accepting the invitation. This is a testing time for the world’s biggest tech companies amidst the many controversies and scandals surrounding them. Facebook has so far taken most of the brunt following the Cambridge Analytica data scandal in March. Google, however, has only recently been coming under more and more scrutiny thanks to scandals like the Mastercard tie-in, revealed by Bloomberg last week. This makes it an odd decision for Pichai to decline his Washington D.C. invitation. Sending your lawyer to answer questions for you is not a good look at a time when how you look has never been more under the microscope. Talking to CNBC last week, Virginia SenatorMark Warner (Democrat), said that "there will be a lot more questions raised that could have been actually dealt with if they sent a senior decision-maker and not simply their counsel." Why did Sundar Pichai decline his invitation from senate? It's not clear why Pichai and Page have declined to testify - Google has, as of yet, failed to comment. The question, then, remains: why are Google executives scared of public scrutiny? Read next DCLeaks and Guccifer 2.0: How hackers used social engineering to manipulate the 2016 U.S. elections Facebook, Twitter takes down hundreds of fake accounts with ties to Russia and Iran, suspected to influence the US midterm elections Sentiment Analysis of the 2017 US elections on Twitter

Huawei has debuted a new Artificial Intelligence chip, the Kirin 980, which boasts a number of world firsts. According to Huawei, the Kirin 980 is the world's first: 7nm process mobile phone SoC chipset Cortex-A76 architecture chipset Dual NPU design Chipset to support LTE Cat.21 with speeds up to 1.4Gbps. The Kirin 980 is less than 1 square centimeter and integrates 6.9 billion transistors. The architecture of the Kirin 980 has eight cores: two are for turbo performance, two are for long-term performance, and the last and smallest four are used to maximize power efficiency. It uses Flex-Scheduling intelligence mechanism to allow the CPU to adapt in heavy, medium and light-load scenarios by reducing its power consumption and giving users a longer battery life. The Kirin 980's performance and efficiency improvements The Kirin 980 uses Mali-G76 GPU improving performance by 46%. The fourth-generation ISP utilises a multi-pass noise reduction to capture quality images and preserve important details. This ISP also has a dedicated video pipeline to effectively improve video clarity and reduce shooting delays by 33%. The Kirin 980 7nm compared with the 10nm process, improves performance by 20%, power efficiency by 40% and the overall energy efficiency of the Kirin 980 by 58%. Huawei has doubled down on its AI processing, adding a dual NPU to the Kirin 980, which performs AI-assisted image recognition tasks at a rate of 4,500 images per minute. The NPU is optimized for vector math that powers machine learning frameworks like Facebook’s Caffe2 and Google’s TensorFlow. Huawei says its heterogeneous computing structure — HiAI — automatically distributes voice recognition, natural language processing, and computer vision workloads across it dynamically. The Kirin 980 will also offer the world’s fastest smartphone Wi-Fi speed, clocking in at 1,732Mbps. Huawei's bid to defeat Qualcomm's Snapdragon 845 The performance improvements you can see in the Kirin 980 are all key to Huawei's plan to oust Qualcomm's Snapdragon 845 as the top mobile AI chip. At the moment, the Snapdragon 845 is the chip that features in Android phones not produced by Huawei. Huawei believes the advantages are significant, claiming its chip has "20 percent better bandwidth and 22 percent lower latency than the Snapdragon 845," while "in gaming applications, the 980 has been shown to produce 22 percent higher frame rates than the 845, and its power consumption when gaming is said to be 32 percent lower" (The Verge). Android Authority have done a detailed fact check looking at the reality of Huawei's claims - it's well worth reading. For a list of other technical specifications, read the Huawei blog. Read next Qualcomm announces a new chipset for standalone AR/VR headsets at Augmented World Expo Tesla is building its own AI hardware for self-driving cars Baidu releases Kunlun AI chip, China’s first cloud-to-edge AI chip

A team of researchers from Institute of Cancer Research London (ICR) and the University of Edinburgh have devised a method named repeated evolution in cancer, also known as REVOLVER. It uses a machine learning approach, specifically known as transfer learning to find out patterns in DNA mutation within cancer and uses the information to forecast future genetic changes. REVOLVER exploits multiple independent noisy observations taken from single patients and transfers information between patients to de-noise data and highlight hidden evolutionary patterns. Along with explaining the data in each patient, the individual models also highlight subgroups of tumors that evolved similarly The goal of this model is to solve the biggest challenge in oncology, that is, the tumor with time could progress from benign to malignant, become metastatic, and develop resistance to certain therapies. This occurs through a process of clonal evolution that involves cancer cells and their microenvironment, and results in intratumor heterogeneity (ITH). ITH results to the deadly outcome of cancer by providing the substrate of phenotypic variation on which adaptation can occur. How REVOLVER works? To accurately detect and compare changes in each tumour, the team used 768 tumour samples from 178 patients reported in previous studies for lung, breast, kidney and bowel cancer, and analysed the data within each cancer type respectively. Source: Nature Methods First, with the help of multi-region sampling genomic ITH is characterized. Patient subgroups share some evolutionary trajectories with common somatic drivers but remain hidden because of apparent variability in genomic patterns between patients. Using the standard approach, the phylogenetic tree (evolutionary model) for every patient is inferred and compared to the n trees. Because the trees are independently inferred, the statistical signal for repeated evolution is weak and few trajectories are identified. REVOLVER uses transfer learning to infer n models jointly and increase their structural correlation. These n trees explain the data in each patient while highlighting repeated evolutionary trajectories in the subgroup. How it will help in cancer treatment? Combining the current knowledge of cancer and identified repeated patterns, scientists could predict the future trajectory of tumour development. This method gives doctor the power of knowing how a tumour will evolve, beforehand, so that they could help the patient in earlier stages. The researchers also found a link between certain sequences of repeated tumour mutations and survival outcome. Repeated patterns of DNA mutations could be used to know the likely of cancer, which could help in shaping future treatment. This method could be used to predict if patients will develop resistance in future, if tumours with certain patterns are found to develop resistance to a particular treatment. Dr Andrea Sottoriva, a team leader in evolutionary genomics and modelling at the ICR who was a part of this study, believes that this AI tool could help the doctors find a treatment in an earlier stage: "By giving us a peek into the future, we could potentially use this AI tool to intervene at an earlier stage, predicting cancer's next move." To explore more on REVOLVER method, check out the paper: Detecting repeated cancer evolution from multi-region tumor sequencing data. Google, Harvard researchers build a deep learning model to forecast earthquake aftershocks location with over 80% accuracy 8 Machine learning best practices How everyone at Netflix uses Jupyter notebooks from data scientists, machine learning engineers, to data analysts

Researchers from Virginia Tech, Chen Gao, Yuliang Zou, and Jia-Bin Huang, recently published a paper on ‘iCAN: Instance-Centric Attention Network for Human-Object Interaction Detection.’ In it, they propose an ‘instance-centric attention module’ (iCAN) for human-object interaction detection. This module uses an incredibly fast regional convolutional neural network (R-CNN), which, in turn, is much more effective in identifying and understanding the human-object interaction. In order to understand the situation in a scene or an image, computers need to recognize how humans interact with surrounding objects. This can be done using human-object interaction, localizes a person and an object, and then well as identifies the relationship - or interaction - between them. The core idea of this research is that an image of a person or an object contains informational cues on the most relevant parts of an image for an algorithm to attend to - this means making predictions should be easier. To exploit this cue, researchers propose an instance-centric attention module that learns to dynamically highlight regions in an image conditioned on the appearance of each instance. Thus, this network allows to selectively aggregate features relevant for recognizing human-object interactions. The researchers validated the efficacy of the proposed network using the COCO and HICO-DET datasets and showed that this approach compares favorably with the state-of-the-art. iCAN module Highlights of the iCAN paper include: The researchers have introduced an instance-centric attention module that allows the network to dynamically highlight informative regions for improving HOI detection. They have also established a new state-of-the-art performance on two large-scale HOI benchmark datasets. They conducted a detailed ablation study and error analysis to identify the relative contributions of the individual components and quantify different types of errors. They also released the source code and pre-trained models to facilitate future research. Advantages of the iCAN module Unlike hand-designed contextual features based on pose, the entire image, or secondary regions, iCAN’s attention map is automatically learned and jointly trained with the rest of the networks for improving the performance. On comparing with attention modules designed for image-level classification, the instance-centric attention map provides greater flexibility as it allows attending to different regions in an image depending on different object instances. To know about iCAN in detail head on to the research paper. Build intelligent interfaces with CoreML using a CNN [Tutorial] CapsNet: Are Capsule networks the antidote for CNNs kryptonite? A new Stanford artificial intelligence camera uses a hybrid optical-electronic CNN for rapid decision making    

The Apollo community has built a machine-learning based auto-calibration system for autonomous driving vehicles. By August 2018, the system had been tested on more than two thousand hours with around ten thousands kilometers’ (6,213 miles) road tests and has proven to be effective. The system is automated and intelligent, due to which, it is suitable for mass-scale self-driving vehicle deployment. Why was Apollo auto-calibration system introduced? Following are the main issues that the current system faces: Manual calibration is time consuming and error prone: The performance and safety of an autonomous driving vehicle depend on its control module. This module includes control algorithms that require vehicle dynamics as input and then sends command to manipulate the vehicle. Performing this calibration in real-time is difficult, that is why, most of the research-oriented autonomous vehicles do manual calibration in one-by-one fashion. Manual calibration consumes a lot of time and is prone to man-made mistakes. Variation in vehicle dynamics: While driving the vehicle dynamics change (i.e. loads change, vehicle parts will be worn out over time, surface friction), and manual calibration cannot possibly cover them. How does Apollo auto-calibration system work? The auto-calibration system depends on the Apollo control module, which consists of an offline model and online learning algorithm Offline model First, a calibration table is generated based on human driving data that best reflects vehicle longitudinal performance at the time of driving. It performs three functions: Collects human driving data Preprocesses the data and select input features Generates calibration table through machine learning models Online learning The online algorithm updates the offline table based on real-time feedback in self-driving mode. It tries to best match the current vehicle dynamics based on offline model established from manual driving data. It performs the following functions: Collects vehicle status and feedback in real time Preprocesses and filter data Adjusts calibration table accordingly To know more details on how this model works and helps to solve the manual calibration problem, check out their published paper: Baidu Apollo Auto-Calibration System - An Industry-Level Data-Driven and Learning based Vehicle Longitude Dynamic Calibrating Algorithm. Apollo 11 source code: A small step for a woman, and a huge leap for ‘software engineering’ Baidu open sources ApolloScape and collaborates with Berkeley DeepDrive to further machine learning in automotives Tesla is building its own AI hardware for self-driving cars  

Following an announcement to developers through App Store Connect last night, Apple now requires all new apps and updates to include a link to their developer’s privacy policy in the app metadata. The new rules will come into effect from October 3rd. Even if the app is a basic utility application that doesn’t connect to the internet, it must still host a website with a privacy policy page. The App Store app listings have had a privacy policy URL metadata field for a long time. They have been mandatory for apps that have subscriptions, but this rule now applies to all apps in the store. Apple will not pull existing apps from sale, but any future update must ensure it has the privacy policy URL set. On iPhone or iPad, customers will be able to tap on the link to the privacy policy and read it in Safari. tvOS does not have a web browser, so developers will have to copy and paste their privacy into a text box when they submit their apps, so the Apple TV can display it. Apple says “The privacy policies must identify what data the app collects, in what manner, and how it is used. It is also the responsibility of the app developer to confirm that the behavior of any embedded third-party frameworks complies with the parent app’s privacy policy. Apple also says that apps must clearly explain data retention policies and detail how a user can revoke consent and request deletion of any personal data stored.” It remains to be seen whether Apple will pull apps that are found to be in violation of their privacy policies. Data-sharing practices in ‘apps’ has come under scrutiny in recent months, in the wake of scandals like Cambridge Analytica. Apple instated informational onboarding screens that describe how it uses personal data earlier this year, as part of European GDPR regulation. The new App Store requirements are likely related to GDPR compliance at some level. A privacy policy is required for App Store distribution, as well as external TestFlight beta testing stages. To read the full story visit the Apple blog page. Apple announces a Special Event to reportedly launch new products including “iPhone XS” and OS updates Could Apple’s latest acquisition yesterday of an AR lens maker signal its big plans for its secret Apple car? Did you know your idle Android device sends data to Google 10 times more often than an iOS device does to Apple?

With a little help from machine learning, you might know what the people on the other end of a Hangouts session are really looking at on their screens. Based on research published at the CRYPTO 2018 Conference in Santa Barbara last week your webcam could give details on what's on your screen, if the person on the other end is listening the right way. All you'll need to do is process the audio picked up by their microphones. Daniel Genkin of the University of Michigan, Mihir Pattani of the University of Pennsylvania, Roei Schuster of Cornell Tech and Tel Aviv University, and Eran Tromer of Tel Aviv University and Columbia University investigated a potential new avenue of remote surveillance dubbed as "Synesthesia”. It is a side-channel attack that can reveal the contents of a remote screen, providing access to potentially sensitive information based solely on "content-dependent acoustic leakage from LCD screens.” Anyone who remembers working with cathode ray tube monitors is familiar with the phenomenon of coil whine. Even though LCD screens consume a lot less power than the old cathode ray tube (CRT), they still generate the same sort of noise, though in a totally different frequency range. Because of the way computer screens render a display—sending signals to each pixel of each line with varying intensity levels for each sub-pixel—the power sent to each pixel fluctuates as the monitor goes through its refresh scans. Variations in the intensity of each pixel create fluctuations in the sound created by the screen's power supply, leaking information about the image being refreshed—information that can be processed with machine learning algorithms to extract details about what's being displayed. That audio could be captured and recorded in a number of ways, as demonstrated by the researchers: Over a device's embedded microphone or an attached webcam microphone during a Skype, Google Hangouts, or other streaming audio chat Through recordings from a nearby device, such as a Google Home or Amazon Echo Over a nearby smartphone; or with a parabolic microphone from distances up to 10 meters Even a reasonably cheap microphone could pick up and record the audio from a display, even though it is just on the edge of human hearing And it turns out that audio can be exploited with a little bit of machine learning black magic. The researchers began by attempting to recognize simple, repetitive patterns. They created a simple program that displays patterns of alternating horizontal black and white stripes of equal thickness (in pixels), which shall be referred to as Zebras, the researchers recounted in their paper. These "zebras" each had a different period, measured by the distance in pixels between black stripes. As the program ran, the team recorded the sound emitted by a Soyo DYLM2086 monitor. With each different period of stripes, the frequency of the ultrasonic noise shifted in a predictable manner. The variations in the audio only really provide reliable data about the average intensity of a particular line of pixels, so it can't directly reveal the content of a screen. However, by applying supervised machine learning in three different types of attacks, the researchers demonstrated that it was possible to extract a surprising amount of information about what was on the remote screen. After training, a neural-network-generated classifier was able to reliably identify which of the Alexa top 10 websites was being displayed on a screen based on audio captured over a Google Hangouts call—with 96.5 percent accuracy. In a second experiment, the researchers were able to reliably capture on-screen keyboard strokes on a display in portrait mode (the typical tablet and smartphone configuration) with 96.4 percent accuracy, for transition times of one and three seconds between key "taps." On a landscape-mode display, accuracy of the classifiers was much lower, with a first-guess success rate of only 40.8 percent. However, the correct typed word was in the top three choices 71.9 percent of the time for landscape mode, meaning that further human analysis could still result in accurate data capture. (The correct typed word was in the top three choices for the portrait mode classifier 99.6 percent of the time.) In a third experiment, the researchers used guided machine learning in an attempt to extract text from displayed content based on the audio—a much more fine-grained sort of data than detecting changes in screen keyboard intensity. In this case, the experiment focused on a test set of 100 English words and also used somewhat ideal display settings for this sort of capture: all the letters were capitalized (in the Fixedsys Excelsior typeface with a character size 175 pixels wide) and black on an otherwise white screen. The results, as the team reported them, were promising: The per-character validation set accuracy (containing 10% of our 10,000 trace collection) ranges from 88% to 98%, except for the last character where the accuracy was 75%. Out of 100 recordings of test words, for two of them preprocessing returned an error. For 56 of them, the most probable word on the list was the correct one. For 72 of them, the correct word appeared in the list of top-five most probable words. While these tests were all done with a single monitor type, the researchers also demonstrated that a "cross screen" attack was possible—by using a remote connection to display the same image on a remote screen and recording the audio, it was possible to calibrate a baseline for the targeted screen. It's clear that there are limits to the practicality of acoustic side-channels as a means of remote surveillance. But as people move to use mobile devices such as smartphones and tablets for more computing tasks—with embedded microphones, limited screen sizes, and a more predictable display environment—the potential for these sorts of attacks could rise. And mitigating the risk would require re-engineering of current screen technology. So, while it remains a small risk, it's certainly one that those working with sensitive data will need to kept in mind—especially if they're spending much time in Google Hangouts with that data on-screen. Read more on this page. Google Titan Security key with secure FIDO two factor authentication is now available for purchase 6 artificial intelligence cybersecurity tools you need to know Defending Democracy Program: How Microsoft is taking steps to curb increasing cybersecurity threats to democracy

Google made the Bitcoin dataset publicly available for analysis in Google BigQuery in February, this year. On the same lines, it announced Ethereum dataset availability in BigQuery, recently, on August 29th for smart contract analytics. Ethereum blockchain is considered as an immutable distributed ledger similar to its predecessor, Bitcoin. However, Vitalik Buterin, Ethereum’s creator, extended Ethereum’s set of capabilities by including a virtual machine that can execute arbitrary code stored on the blockchain as smart contracts. The Ethereum blockchain data are now available for exploration with BigQuery. All historical data are in the ethereum_blockchain dataset, which updates daily. Need for Ethereum blockchain data availability on Google Cloud Ethereum blockchain peer-to-peer software has an API for a subset of commonly used random-access functions, for instance, checking transaction status, looking up wallet-transaction associations, and checking wallet balances. API endpoints neither exist for easy access to the data stored on-chain, nor for viewing the blockchain data in aggregate.  Given below is an example chart showing the total Ether transferred, and average transaction cost, aggregated by day: Source: Google Such a visualization, underpinned with a database query aids in making business decisions, such as prioritizing improvements to the Ethereum architecture itself to balance sheet adjustments. BigQuery has strong OLAP capabilities to support such an analysis during ad-hoc and in general situations. Also, this does not require additional API implementation. Accordingly, Google built a software system on Google Cloud that: Synchronizes the Ethereum blockchain to computers running Parity in Google Cloud. Performs a daily extraction of data from the Ethereum blockchain ledger, including the results of smart contract transactions, such as token transfers. De-normalizes and stores date-partitioned data to BigQuery for easy and cost-effective exploration. Google has also demonstrated a number of interesting queries and visualizations based on the Ethereum dataset. The analysis focus on three topics: Smart contract function calls On-chain transaction time-series and transaction networks Smart contract function analytics The Ethereum blockchain dataset is also available on Kaggle. You can query the live data in Kernels, Kaggle’s no charge in-browser coding environment, using the BigQuery Python client library. The Ethereum ETL project on GitHub contains all source code used to extract data from the Ethereum blockchain and load it into BigQuery. Read more about this news in detail on Google Cloud blog. Vitalik Buterin’s new consensus algorithm to make Ethereum 99% fault tolerant How to set up an Ethereum development environment [Tutorial] Everything you need to know about Ethereum

This week's Tech news was dominated by stories that show how intricately tech (big tech and open source alike), governments, and societies are tied together. The subtext in these stories is the clash of culture, individual and group goals amplified by tech, for better and for worse. Furthermore, the Tech news stories demonstrate some of the ways that we can help create a Tech culture that matters, that actually does much more than what it's told to. Lerna relicensing its project earlier this week to protest ICE (they rolled back on that decision today), Facebook banning Myanmar military personnel over fake news, and (alongside Twitter) taking down fake accounts to prevent cyber interference in US midterms, are all examples of how software organizations can play a part in addressing wider societal issues and problems. Salient software releases in this week's Tech news include Kubernetes project management change, Google's Dopamine, Golang 1.11, JDK RC1, Arduino CLI, MagicLeap One, and Project Zowe. Subscribe to our weekly newsletter for highlights on the major Tech news stories that unfolded in the week or visit our Tech news page for full details. Click on the below image to access this week's newsletter. In other Tech news And here are other things that happened in Tech news this week. Sangoma Technologies is acquiring Digium Incorporation Sangoma Technologies Corporation has entered into a definitive agreement on August 23, 2018, to acquire all of the outstanding shares of Digium, Inc. This transaction is supposed to add meaningful sales, create market leadership, and increase recurring revenue materially. Read more on Sangoma Blog. The next version of .NET Standard will be versioned as 2.1 The size of the API surface feels incremental, thus it's the most natural choice after. It is an obvious choice when looked at purely from .NET Standard and underlines messaging that .NET Standard and .NET Core are versioned independently. Read more on Github. YouTube launches ‘Time watched’ stats and central controls for Digital Wellbeing menu on Android/iOS YouTube is rolling out “Time watched” stats along with centralized controls for Digital Wellbeing features like break reminders and notification management. Read more on 9TO5Google. Twitter removes a total to 770 accounts linked Russian political meddling Twitter has suspended a total of 770 accounts for “coordinated inauthentic behavior.” These accounts were generally networks of independent outlets that were in fact controlled centrally by Russia and Iran. Facebook has also successfully taken down 652 fake accounts and pages that published political content. Read more on Techcrunch. Facebook plans to power global operations with 100% renewable energy by the end of 2020 Facebook is on track to be one of the largest corporate purchasers of renewable energy. It is also committing to reducing greenhouse gas emissions by 75% and powering its global operations with 100% renewable energy by the end of 2020. Read more on Facebook newsroom. Andrew Moore steps down as dean of CMU's School of Computer Science Andrew Moore is stepping down from his role as dean at Carnegie Mellon University’s School of Computer Science, having served as dean since August 2014. He is leaving to pursue a new professional opportunity. Read more on Pittsburgh Post-Gazette. Bernie Sanders calls out Jeff Bezos for poor treatment of Amazon workers Bernie Sanders called out Jeff Bezos for poor treatment of Amazon workers. In a rare move, the company fired back saying Sanders’s allegations of low wages and indecent work conditions are “inaccurate and misleading.” Read more at Vox. Artificial Intelligence helps Schools monitor students' mental health With the help of mental-health experts, the company trained a machine-learning algorithm called GoGuardian which flags content most closely associated with potential harm, like searches for suicide methods. Read more on Axios. California Net Neutrality Passes State Assembly After a long and hard-fought battle, California’s Assembly passed S.B. 822, the net neutrality bill. In a bipartisan vote of 61-18, S.B. 822 passed the Assembly. Now it needs to pass the Senate again. Read more on EFF. Java is still available at zero-cost With Java 9 and the six-monthly release cycle, free support for the project is now much more tightly controlled. Oracle announced that there will be no more free Java SE 8 updates for commercial use after January 2019.  For individual personal use,  public updates for Oracle Java SE 8 will be available at least till December 2020. Read more on Stephen Colebourne blog. Linux kernel developers criticize Intel's initial disclosure of the Meltdown and Spectre CPU vulnerabilities. This week at the Open Source Summit North America, one of the world's leading Linux kernel developers took issue with Intel's initial disclosure of the Meltdown and Spectre CPU vulnerabilities. Spectre, which exploits the speculative execution mechanism employed in modern processor chips and has recently targeted Intel, AMD, and ARM. Read more on eWEEK. Other new tool releases and announcements this week For details on this week’s major tool releases and announcements, check out this week’s newsletter. Below are more tool updates that happened this week. Distillery 2.0 released Distillery 2.0 has been released with runtime configuration. better support for custom appups, support for generating PID files, improved errors and better feedback from the CLI and more. Read more on Dockyard. A new Vulkan-based GPGPU computing framework Vuh is a new Vulkan-based GPGPU computing framework. Vuh aims to reduce the boilerplate to (a reasonable) minimum in most common GPGPU computing scenarios. The ultimate goal is to beat OpenCL in usability, portability and performance. Read more on Github. Raspberry Pi Power over Ethernet (PoE) hat is now out Raspberry Pi 3 Model B+ has a  new board with the ability to be powered through Power over Ethernet (PoE) with a HAT. Now the PoE HAT is on sale. The HAT connects to the Raspberry Pi 3B+ 0.1” headers: the 40-way GPIO; and the new 4-pin header near the USB connectors, which allows you to power the system using your Ethernet cable. Read more on Raspberry blog. Introducing F2, an elegant, interactive and flexible charting library for mobile F2 is a free and open-source modern charting library, released under MIT license. The aim of F2 is helping developers to create interactive visualizations for mobile devices more easier.  Read more on medium. Databot, a new Python Data-driven programming framework Databot is a high-performance Data-driven programming framework with paralleled in coroutines and ThreadPool. It also includes Type- and content-based route function. Read more on Github. NVIDIA Tesla V100 GPUs are now generally available NVIDIA Tesla V100 GPUs with NVLink is now generally available. V100 together with our K80, P100, and P4 GPUs, are all great for speeding up many CUDA-powered compute and HPC workloads. Read more on Google cloud blog. Scala 2.13.0-M5 has been released M5 is their feature-freeze release for Scala 2.13. From here forward, the team will close existing open loops but not embark on or accept new work. Read more on Github. billboard.js 1.6.0 is now released Billboard 1.6 comes with 10 new features and 19 bug fixes including changes to themes, dasharray for regions, enhancement on custom data point, and more. Read more on medium.

When we hear  the word robot, we may think of large industry sized robots assembling cars or humanoid ones. However there are such tiny robots that you may not even be able to see with the naked eye. Such six microbots are covered in this article which are in early development stages. Harvard's Ambulatory Microrobot (HAMR): A robotic cockroach Source: Hardvard HAMR is a versatile, 1.8-inch-long robotic platform that resembles a cockroach. The HAMR itself weighs in under an ounce and can run, jump and carry small items about twice its own weight. It is fast and can move with the speed of almost 19 inches per second. HAMR has given the researchers a useful base idea from which they can build other ideas. For example, the HAMR-F, an enhanced version of HAMR doesn't have any restraining wires. It can move around independently, it's only slightly heavier (2.8g) and slower than the HAMR. It is powered by a micro 8mA lithium polymer battery. Scientists at Harvard's School of Engineering and Applied Sciences also added footpads recently that allows the microbot to swim on water surface, sink and walk under water. Robotic bees: RoboBees Source: Harvard Like the HAMR, the RoboBee by Harvard has improved over time, it can also fly and swim. Its first successful flight was in 2013 and in 2015 it was able to swim. More recently in 2016, it gained the ability to "perch" on surfaces using static electricity. This allows the RoboBee to save power for loner flights. The 80-milligram robot can take a swim, leap up from the water, and then land. The RoboBee can flap its wings at 220 to 300 hertz in air and 9 to 13 hertz in water. μRobotex: microbots from France Source: Sciencealert Scientists from the Femto-ST Institute in France have built the μRobotex platform. It is a new, extremely small microrobot system. This system has been able to build the smallest house in the world inside a vacuum chamber. The robot used an ion beam to cut a silica membrane to tiny pieces for assembly. The micro house is 0.015 mm high and 0.020 mm broad. In comparison, a grain of sand is anywhere from 0.05 mm to 2 mm in diameter. The completed house was kept on the tip of an optical fiber piece as shown in the image above. Salto: a one-legged jumper Source: Wired Saltatorial locomotion on terrain obstacles (Salto), developed at University of California, is a one-legged jumping robot that is 10.2 inches tall when fully extended. It weighs about 100 grams, and can jump up to 1 meter in air. Salto's skills show when it can do more than just a single jump. It can bounce off walls and can perform several jumps in a row while avoiding obstacles. Salto was inspired by the galago, a small mammal expert at jumping. The idea of Salto was about robots that can leap over rubble, to provide emergency services. The newer model is the Salto-1P. Rolls Royce’s SWARM robots Source: Rolls Royce Rolls-Royce teamed up with scholars from the University of Nottingham and Harvard University to develop independent tiny mobile robots called SWARM. They are about 0.4 inches in diameter. They are a part of Rolls-Royce’s IntelligentEngine program. The SWARM robots are put into position by a robotic snake and use tiny cameras to capture parts of an engine which are hard to access otherwise. This is very useful for mechanics to figure out what is wrong with a car engine with greater accessibility. The future plan for SWARM is to perform inspections of aircraft engines in order to not remove from the airplanes. Short-Range Independent Microrobotic Platforms (SHRIMP) Source: DARPA The Defense Advanced Research Project Agency (DARPA) wants to develop insect-scaled robots with, "untethered mobility, maneuverability, and dexterity." In other words, they want microbots that can move around independently. DARPA is planning to sponsor these robots as part of the SHRIMP program for search and rescue, disaster relief, and hazardous environment inspection. It is also looking for robots that might work as prosthetics or eyes to see in places that are hard to reach. These microbots are in early development stages but on entering production they will be very resourceful. From medical assistance to guided inspection in small areas, these microbots will prove to be useful in a variety of areas. Intelligent mobile projects with TensorFlow: Build a basic Raspberry Pi robot that listens, moves, sees, and speaks [Tutorial] 15 millions jobs in Britain at stake with AI robots set to replace humans at workforce What Should We Watch Tonight? Ask a Robot, says Matt Jones from OVO Mobile [Interview]

Anima Anandkumar has now bid adieu to AWS after working as the principal scientist at Amazon Web Services (AWS). She joined AWS in November 2016, as Principal Scientist on Deep Learning. She is best known for her work in the development and analysis of tensor algorithms and in the design, development, and launch of Amazon SageMaker. Anima has earned several prestigious awards, including the Alfred P. Sloan Research Fellowship, the NSF CAREER award, and Young Investigator Research award. After her successful 2 year stint in Amazon AWS, she has left her current post and written a heartwarming post on her personal blog. In her own words, “I want to recollect the rich learning experiences I had and the amazing things we accomplished over the last two years.” Amazon was Anima’s first industry job out of academia. She saw huge potential to democratize AI and hence chose AWS, it is the most comprehensive and broadly adopted cloud platform. During her tenure at Amazon she worked on the latest GPU instances, Deeplens,  and on computer vision, natural language processing, speech recognition and other technologies. Her most important contribution, however, remains, Amazon SageMaker. Its broad adoption led to AWS increasing its ML user base by more than 250 percent over the last year. Anima says, “It was personally fulfilling to build topic modeling on SageMaker (and AWS comprehend) based on my academic research, which uses tensor decompositions. SageMaker topic-modeling automatically categorizes documents at scale and is several times faster than any other (open-source) framework. Taking the tensor algorithm from its theoretical roots to an AWS production service was a big highlight for me.” As a part of applied research at AWS, she has worked on deep active learning, crowdsourcing and semi-supervised learning methods in a number of domains. She contributed to Amazon community outreach by building partnerships with universities and non-profit organizations to democratize AI.  She also represented AWS at many prominent avenues, including Deep Learning Indaba 2017, the first pan-African deep learning summit, Mulan forum for Chinese women entrepreneurs, Geekpark forum for startups in China and Shaastra 2018 at IIT Madras in India. Anima has always been a supporter of women in tech. When Anima went to IIT Madras, she realized the fewer number of women around her (the female to male ratio at IIT Madras was 1:20 then). “Even though I missed having more women in IIT, the women who got in there were remarkable since they overcame other barriers and still performed well; it gave a lot of confidence. Though I do wish there were more women and I'm always looking how to improve the diversity, it should be towards helping women overcome barriers (without compromising on performance/quality).” Her contributions make us realize the fact that women in tech are an important facet even though they are in smaller numbers. Read Anima’s adieu blog for a trip down her memory lane at AWS Cloud. Apollo 11 source code: A small step for a woman, and a huge leap for ‘software engineering’. “Technology opens up so many doors” – An Interview with Sharon Kaur from School of Code. Netflix brings in Verna Myers as new VP of Inclusion strategy to boost cultural diversity.

Google and Harvard have teamed up to find out a way to predict locations where the earthquake aftershocks might occur, with the help of deep learning. Currently, it is possible to only predict the timing and size of aftershocks with the help of empirical laws such as, Bäth's Law and Omori's Law. However, forecasting where these events will occur is more challenging. The researchers at Google, with Brendan Meade, a professor of Earth and Planetary Sciences at Harvard University, and Phoebe DeVries, a post-doctoral fellow working in his lab, are using machine learning to try finding a way to forecast the location where the aftershock occurs. DeVries believes that forecasting aftershock is a well-suited problem for machine learning to solve: "I'm very excited for the potential for machine learning going forward with these kind of problems -- it's a very important problem to go after. Aftershock forecasting in particular is a challenge that's well-suited to machine learning because there are so many physical phenomena that could influence aftershock behavior and machine learning is extremely good at teasing out those relationships. I think we've really just scratched the surface of what could be done with aftershock forecasting...and that's really exciting." How does this deep learning algorithm work? They started with a database consisting of information of nearly 118 major earthquakes from around the world. Next, they applied neural network to analyze the relationships between static changes caused by mainshocks and aftershock locations. The algorithm was able to extract useful patterns from the data. Finally, they got an improved model to forecast aftershock locations. This model is not absolutely precise, but proved to be significantly more reliable than most of the existing models like, Coulomb failure stress change. In terms of accuracy the deep learning model was able to hit 0.849, on an accuracy scale of 0 to 1. They have also published a paper documenting their findings. What are the future applications of this model? The deep learning-based model will help deploy emergency services such as  structural modifications and storing supplies and emergency kit. It will help in making informed evacuation plans for areas at risk of an aftershock, beforehand. The model is far from ready to deploy in the real-world, but has definitely given a motivation to the researchers to investigate the relevance of deep learning in mitigating earthquake aftershocks. To know more on how Google and Harvard teamed up to solve the problem of earthquake aftershocks using deep learning check out Google’s blog post. AutoAugment: Google’s research initiative to improve deep learning performance Deep Learning in games – Neural Networks set to design virtual worlds Google strides forward in deep learning: open sources Google Lucid to answer how neural networks make decisions

This week California's net neutrality bill passed through the California State Assembly. The bill went through on a vote of 61-18 - it will now move on to the senate (again), where a vote will likely happen next week. California's net neutrality bill is one of a number of state level responses to the FCC's decision to repeal the existing legislation (Washington and Oregon are the only 2 states to have passed full net neutrality bills). It's believed to be the toughest net neutrality bill in the U.S. This is because as well as preventing ISPs from throttling traffic, and stopping them from charging websites for special access to internet users, it also bans "zero rating" on certain apps (which is where using certain apps won't count against a user's data usage). Miguel Santiago, (D-Los Angeles) said, when presenting the bill, that "“The Trump administration destroyed the internet as we know it, plain and simple... We have an opportunity in California to lead this nation by voting yes for this bill.” However, there was some criticism of the bill from Republicans. For example, Jim Paterson, Republican Assemblymember for Fresno, argued that the argument needs to be resolved at a federal level. “The worst possible thing we can do is have created 50 different state FCCs." The EFF responds to California net neutrality vote As you might expect, the EFF - the Electronic Frontier Foundation - was jubilant at the result. "You did it" exclaimed the title of a blog post published on the organization's website on Thursday. "ISPs have tried hard to gut and kill this bill, pouring money and robocalls into California. There was a moment where that campaign looked like it might have been successful, but you spoke out and got strong net neutrality protections restored. But that hiccup means that, although a version of the bill already passed in the California Senate, it’s now different enough from that initial version to have to be re-voted on." The EFF urged people in California to "contact your California state senator and tell them to vote yes." "California can prove that ISP money can’t defeat real people’s voices." Find out more about what you can do to support the net neutrality bill in California here. Read next Furthering the Net Neutrality debate, GOP proposes the 21st Century Internet Act Google releases new political ads library as part of its transparency report